Description: Overlapping confidence intervals refer to a situation where the confidence intervals of two statistical estimates intersect, indicating uncertainty about the difference between these estimates. In statistical terms, a confidence interval is a range of values used to estimate a population parameter, such as the mean or proportion, with a specific confidence level, typically 95% or 99%. When the confidence intervals of two groups or samples overlap, it suggests that there is insufficient evidence to assert that a significant difference exists between them. This is crucial in interpreting results in comparative studies, as overlap may lead to the conclusion that observed differences are due to chance. Evaluating overlapping confidence intervals is a fundamental tool in data science and applied statistics, allowing researchers and analysts to assess the robustness of their findings and make informed decisions based on the inherent uncertainty of the data. In summary, overlapping confidence intervals are a key indicator of the relationship between two estimates and suggest the need for deeper analysis to understand the implications of the results obtained.
